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    Characterizing Patterns of Nurses’ Daily Sleep Health: A Latent Profile Analysis

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    Name:
    LPA paper nurse sleep 12-1-2021 ...
    Size:
    381.2Kb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
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    Author
    Slavish, Danica C.
    Contractor, Ateka A.
    Dietch, Jessica R.
    Messman, Brett
    Lucke, Heather R.
    Briggs, Madasen
    Thornton, James
    Ruggero, Camilo
    Kelly, Kimberly
    Kohut, Marian
    Taylor, Daniel J.
    Show allShow less
    Affiliation
    University of Arizona
    Issue Date
    2022-01-05
    Keywords
    Latent profile analysis
    Longitudinal
    Nightmares
    Nurses
    Sleep diary
    
    Metadata
    Show full item record
    Publisher
    Springer Science and Business Media LLC
    Citation
    Slavish, D. C., Contractor, A. A., Dietch, J. R., Messman, B., Lucke, H. R., Briggs, M., Thornton, J., Ruggero, C., Kelly, K., Kohut, M., & Taylor, D. J. (2022). Characterizing Patterns of Nurses’ Daily Sleep Health: A Latent Profile Analysis. International Journal of Behavioral Medicine.
    Journal
    International Journal of Behavioral Medicine
    Rights
    © International Society of Behavioral Medicine 2021.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Background: Nursing is a demanding occupation characterized by dramatic sleep disruptions. Yet most studies on nurses’ sleep treat sleep disturbances as a homogenous construct and do not use daily measures to address recall biases. Using person-centered analyses, we examined heterogeneity in nurses' daily sleep patterns in relation to psychological and physical health. Methods: Nurses (N = 392; 92% female, mean age = 39.54 years) completed 14 daily sleep diaries to assess sleep duration, efficiency, quality, and nightmare severity, as well as measures of psychological functioning and a blood draw to assess inflammatory markers interleukin-6 (IL-6) and C-reactive protein (CRP). Using recommended fit indices and a 3-step approach, latent profile analysis was used to identify the best-fitting class solution. Results: The best-fitting solution suggested three classes: (1) “Poor Overall Sleep” (11.2%), (2) “Nightmares Only” (8.4%), (3) “Good Overall Sleep” (80.4%). Compared to nurses in the Good Overall Sleep class, nurses in the Poor Overall Sleep or Nightmares Only classes were more likely to be shift workers and had greater stress, PTSD symptoms, depression, anxiety, and insomnia severity. In multivariate models, every one-unit increase in insomnia severity and IL-6 was associated with a 33% and a 21% increase in the odds of being in the Poor Overall Sleep compared to the Good Overall Sleep class, respectively. Conclusion: Nurses with more severe and diverse sleep disturbances experience worse health and may be in greatest need of sleep-related and other clinical interventions.
    Note
    12 month embargo; published: 05 January 2022
    ISSN
    1070-5503
    EISSN
    1532-7558
    DOI
    10.1007/s12529-021-10048-4
    Version
    Final accepted manuscript
    Sponsors
    national institute of allergy and infectious diseases
    ae974a485f413a2113503eed53cd6c53
    10.1007/s12529-021-10048-4
    Scopus Count
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    UA Faculty Publications

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